Key Factors Assessment on Bird Strike Density Distribution in Airport Habitats: Spatial Heterogeneity and Geographically Weighted Regression Model

被引:5
|
作者
Shao, Quan [1 ]
Zhou, Yan [1 ]
Zhu, Pei [1 ]
Ma, Yan [2 ]
Shao, Mengxue [1 ]
机构
[1] Nanjing Univ Aeronaut & Astronaut, Coll Civil Aviat, Coll Flight, Nanjing 210016, Peoples R China
[2] Peking Univ, Coll Urban & Environm Sci, Beijing 100871, Peoples R China
基金
中国国家自然科学基金;
关键词
bird strike density; spatial heterogeneity; key factors; airport habitats; GWR model; CO2; EMISSIONS; CHINA; RISK; URBANIZATION; COMMUNITIES; SPACE; RED;
D O I
10.3390/su12187235
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Although the factors influencing bird strikes have been studied extensively, few works focused on the spatial variations in bird strikes affected by factors due to the difference in the geographical environment around the airport. In this paper, the bird strike density distribution of different seasons affected by factors in a rectangular region of 800 square kilometers centered on the Xi'an Airport runway was investigated based on collected bird strike data. The ordinary least square (OLS) model was used to analyze the global effects of different factors, and the Geographically Weighted Regression (GWR) model was used to analyze the spatial variations in the factors of bird strike density. The results showed that key factors on the kernel density of bird strikes showed evident spatial heterogeneity and the seasonal difference in the different habitats. Based on the results of the study, airport managers are provided with some specific defense measures to reduce the number of bird strikes from the two aspects of expelling birds on the airfield area and reducing the attractiveness of habitats outside the airport to birds, so that achieve the sustainable and safe development of civil aviation and the ecological environment.
引用
收藏
页码:1 / 16
页数:16
相关论文
共 17 条
  • [1] Spatial heterogeneity of factors influencing transportation CO2emissions in Chinese cities: based on geographically weighted regression model
    Wang, Huiping
    Zhang, Xueying
    AIR QUALITY ATMOSPHERE AND HEALTH, 2020, 13 (08) : 977 - 989
  • [2] Spatial heterogeneity of factors influencing transportation CO2 emissions in Chinese cities: based on geographically weighted regression model
    Huiping Wang
    Xueying Zhang
    Air Quality, Atmosphere & Health, 2020, 13 : 977 - 989
  • [3] Spatial distribution characteristics and influencing factors of soil organic carbon based on the geographically weighted regression model
    Shu, Xin
    Gao, Liangmin
    Yang, Jinxiang
    Xia, Jieyu
    Song, Han
    Zhu, Limei
    Zhang, Kai
    Wu, Lin
    Pang, Zhendong
    ENVIRONMENTAL MONITORING AND ASSESSMENT, 2024, 196 (11)
  • [4] Spatial Distribution Characteristics of Species Diversity Using Geographically Weighted Regression Model
    Park, Jeongmook
    Choi, Byoungkoo
    Lee, Jungsoo
    SENSORS AND MATERIALS, 2019, 31 (10) : 3197 - 3213
  • [5] The Factors Influencing China's Population Distribution and Spatial Heterogeneity: a Prefectural-Level Analysis using Geographically Weighted Regression
    Xu, Zhibin
    Ouyang, Anjiao
    APPLIED SPATIAL ANALYSIS AND POLICY, 2018, 11 (03) : 465 - 480
  • [6] Using Contextualized Geographically Weighted Regression to Model the Spatial Heterogeneity of Land Prices in Beijing, China
    Harris, Rich
    Dong, Guanpeng
    Zhang, Wenzhong
    TRANSACTIONS IN GIS, 2013, 17 (06) : 901 - 919
  • [7] The Factors Influencing China’s Population Distribution and Spatial Heterogeneity: a Prefectural-Level Analysis using Geographically Weighted Regression
    Zhibin Xu
    Anjiao Ouyang
    Applied Spatial Analysis and Policy, 2018, 11 : 465 - 480
  • [8] Exploring the spatial heterogeneity of urban heat island effect and its relationship to block morphology with the geographically weighted regression model
    Gao, Yuejing
    Zhao, Jingyuan
    Han, Li
    SUSTAINABLE CITIES AND SOCIETY, 2022, 76
  • [9] Spatial heterogeneity of the associations of economic and health care factors with infant mortality in China using geographically weighted regression and spatial clustering
    Wang, Shaobin
    Wu, Jun
    SOCIAL SCIENCE & MEDICINE, 2020, 263
  • [10] Uncovering spatial heterogeneity in real estate prices via combined hierarchical linear model and geographically weighted regression
    Hu, Yigong
    Lu, Binbin
    Ge, Yong
    Dong, Guanpeng
    ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE, 2022, 49 (06) : 1715 - 1740